Consistent and robust determination of border ownership based on asymmetric surrounding contrast

Sakai, Ko; Nishimura, Hirokazu; Shimizu, Ryohei; Kondo, Keiichi

doi:10.1016/j.neunet.2012.05.006

Cited by 33 publications

(51 citation statements)

References 51 publications

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“…Comparing the responses of individual neurons across large samples of different natural scenes, we found that some neurons were 90% consistent across scenes. These V2 neurons perform much better than the simulated neurons in a somewhat simplistic model (Sakai et al, 2012), which reached only 67% consistency on images of the Berkeley Segmentation Dataset. We are not aware of other neural models that have been evaluated on images of natural scenes.…”

Section: Discussionmentioning

confidence: 84%

“…On the other hand, these images are infinitely more complex than displays of simple geometrical figures. Models of border-ownership coding were mostly designed for geometrical figures (Zhaoping, 2005; Sakai and Nishimura, 2006; Craft et al, 2007), and when such models were tested on complex natural scenes, their performance was modest (Sakai et al, 2012; Russell et al, 2014). …”

Section: Introductionmentioning

confidence: 99%

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Figure-Ground Organization in Visual Cortex for Natural Scenes

Williford

Heydt

2016

eNeuro

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Figure-ground organization and border-ownership assignment are essential for understanding natural scenes. It has been shown that many neurons in the macaque visual cortex signal border-ownership in displays of simple geometric shapes such as squares, but how well these neurons resolve border-ownership in natural scenes is not known. We studied area V2 neurons in behaving macaques with static images of complex natural scenes. We found that about half of the neurons were border-ownership selective for contours in natural scenes, and this selectivity originated from the image context. The border-ownership signals emerged within 70 ms after stimulus onset, only ∼30 ms after response onset. A substantial fraction of neurons were highly consistent across scenes. Thus, the cortical mechanisms of figure-ground organization are fast and efficient even in images of complex natural scenes. Understanding how the brain performs this task so fast remains a challenge.

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Section: Discussionmentioning

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Figure-Ground Organization in Visual Cortex for Natural Scenes

Williford

Heydt

2016

eNeuro

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“…Border-ownership information could be generated from the asymmetric organization of surrounds (Nishimura and Sakai, 2004, 2005; Sakai et al, 2012) or from a diffusion-like process within the image representation (Grossberg, 1994, 1997; Baek and Sajda, 2005; Kikuchi and Akashi, 2001; Pao et al, 1999; Zhaoping, 2005). However, these models have difficulties explaining the fast establishment of border ownership which appears about 25ms after the first stimulus response (Zhou et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

A recurrent neural model for proto-object based contour integration and figure-ground segregation

Niebur

2017

J Comput Neurosci

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Visual processing of objects makes use of both feedforward and feedback streams of information. However, the nature of feedback signals is largely unknown, as is the identity of the neuronal populations in lower visual areas that receive them. Here, we develop a recurrent neural model to address these questions in the context of contour integration and figure-ground segregation. A key feature of our model is the use of grouping neurons whose activity represents tentative objects (“proto-objects”) based on the integration of local feature information. Grouping neurons receive input from an organized set of local feature neurons, and project modulatory feedback to those same neurons. Additionally, inhibition at both the local feature level and the object representation level biases the interpretation of the visual scene in agreement with principles from Gestalt psychology. Our model explains several sets of neurophysiological results (Zhou et al, 2000; Qiu et al, 2007; Chen et al, 2014), and makes testable predictions about the influence of neuronal feedback and attentional selection on neural responses across different visual areas. Our model also provides a framework for understanding how object-based attention is able to select both objects and the features associated with them.

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“…Our previous computational and psychophysical studies have indicated that the surrounding suppression/facilitation observed in early visual areas (Jones et al, 2001, 2002; Ozeki et al, 2009) plays crucial roles in the responses of BO-selective cells (Sakai and Nishimura, 2006; Sakai et al, 2012). Specifically, we showed that early-level features such as luminance contrast around the CRF are capable of allocating BO in a manner similar to the physiological observations.…”

Section: Introductionmentioning

confidence: 99%

“…Top-down spatial attention increased the responses by the PP to enhance the representation of the attended location (Rolls and Deco, 2002; Deco and Lee, 2004). Feedback from the PP module altered the contrast gain in the V1 module, which, in turn, modulated the activities of BO-selective model cells in the V2 module as the responses of these cells were determined by the surrounding suppression/facilitation based on early-level features extracted in the V1 (Sakai and Nishimura, 2006; Sakai et al, 2012). Herein, we performed the simulations of our model with various visual inputs, which corresponded to physiological and psychophysical experiments.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Time-Course of Responses for the Border Ownership Selectivity Based on the Integration of Feedforward Signals and Visual Cortical Interactions

Wagatsuma

Sakai

2017

Front. Psychol.

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Border ownership (BO) indicates which side of a contour owns a border, and it plays a fundamental role in figure-ground segregation. The majority of neurons in V2 and V4 areas of monkeys exhibit BO selectivity. A physiological work reported that the responses of BO-selective cells show a rapid transition when a presented square is flipped along its classical receptive field (CRF) so that the opposite BO is presented, whereas the transition is significantly slower when a square with a clear BO is replaced by an ambiguous edge, e.g., when the square is enlarged greatly. The rapid transition seemed to reflect the influence of feedforward processing on BO selectivity. Herein, we investigated the role of feedforward signals and cortical interactions for time-courses in BO-selective cells by modeling a visual cortical network comprising V1, V2, and posterior parietal (PP) modules. In our computational model, the recurrent pathways among these modules gradually established the visual progress and the BO assignments. Feedforward inputs mainly determined the activities of these modules. Surrounding suppression/facilitation of early-level areas modulates the activities of V2 cells to provide BO signals. Weak feedback signals from the PP module enhanced the contrast gain extracted in V1, which underlies the attentional modulation of BO signals. Model simulations exhibited time-courses depending on the BO ambiguity, which were caused by the integration delay of V1 and V2 cells and the local inhibition therein given the difference in input stimulus. However, our model did not fully explain the characteristics of crucially slow transition: the responses of BO-selective physiological cells indicated the persistent activation several times longer than that of our model after the replacement with the ambiguous edge. Furthermore, the time-course of BO-selective model cells replicated the attentional modulation of response time in human psychophysical experiments. These attentional modulations for time-courses were induced by selective enhancement of early-level features due to interactions between V1 and PP. Our proposed model suggests fundamental roles of surrounding suppression/facilitation based on feedforward inputs as well as the interactions between early and parietal visual areas with respect to the ambiguity dependence of the neural dynamics in intermediate-level vision.

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Consistent and robust determination of border ownership based on asymmetric surrounding contrast

Cited by 33 publications

References 51 publications

Figure-Ground Organization in Visual Cortex for Natural Scenes

Figure-Ground Organization in Visual Cortex for Natural Scenes

A recurrent neural model for proto-object based contour integration and figure-ground segregation

Modeling the Time-Course of Responses for the Border Ownership Selectivity Based on the Integration of Feedforward Signals and Visual Cortical Interactions

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